Task 1
library(ggplot2)
library(tibble)
mpg
## # A tibble: 234 × 11
## manufacturer model displ year cyl trans drv cty hwy fl class
## <chr> <chr> <dbl> <int> <int> <chr> <chr> <int> <int> <chr> <chr>
## 1 audi a4 1.8 1999 4 auto… f 18 29 p comp…
## 2 audi a4 1.8 1999 4 manu… f 21 29 p comp…
## 3 audi a4 2 2008 4 manu… f 20 31 p comp…
## 4 audi a4 2 2008 4 auto… f 21 30 p comp…
## 5 audi a4 2.8 1999 6 auto… f 16 26 p comp…
## 6 audi a4 2.8 1999 6 manu… f 18 26 p comp…
## 7 audi a4 3.1 2008 6 auto… f 18 27 p comp…
## 8 audi a4 quattro 1.8 1999 4 manu… 4 18 26 p comp…
## 9 audi a4 quattro 1.8 1999 4 auto… 4 16 25 p comp…
## 10 audi a4 quattro 2 2008 4 manu… 4 20 28 p comp…
## # ℹ 224 more rows
mpg |>
ggplot(aes(displ, hwy, colour = drv)) +
geom_point()
mpg |>
ggplot(aes(displ, hwy, colour = drv, size = cyl)) +
geom_point(show.legend = FALSE) +
theme_void() +
scale_color_brewer()
mpg |>
ggplot(aes(displ, hwy, colour = drv)) +
geom_point(show.legend = FALSE, size = 4) +
geom_point(show.legend = FALSE, size = 1, colour = "#222222") +
coord_polar() +
theme_void() +
scale_color_brewer()
Exercise 1
mpg |>
ggplot(aes(hwy, cty, colour = drv)) +
geom_point(show.legend = FALSE, size = 7) +
geom_point(show.legend = FALSE, size = 0.5, colour = "lightyellow") +
coord_polar() +
theme_void() +
scale_color_brewer()
End of Exercise 1
set.seed(1)
n <- 50
dat <- tibble(
x0 = runif(n),
y0 = runif(n),
x1 = x0 + runif(n, min = -.2, max = .2),
y1 = y0 + runif(n, min = -.2, max = .2),
shade = runif(n),
size = runif(n)
)
dat
## # A tibble: 50 × 6
## x0 y0 x1 y1 shade size
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.266 0.478 0.327 0.523 0.268 0.762
## 2 0.372 0.861 0.313 0.884 0.219 0.933
## 3 0.573 0.438 0.481 0.370 0.517 0.471
## 4 0.908 0.245 1.11 0.226 0.269 0.604
## 5 0.202 0.0707 0.255 0.0709 0.181 0.485
## 6 0.898 0.0995 0.784 -0.0282 0.519 0.109
## 7 0.945 0.316 0.796 0.328 0.563 0.248
## 8 0.661 0.519 0.652 0.349 0.129 0.499
## 9 0.629 0.662 0.799 0.573 0.256 0.373
## 10 0.0618 0.407 0.101 0.292 0.718 0.935
## # ℹ 40 more rows
dat |>
ggplot(aes(
x = x0,
y = y0,
xend = x1,
yend = y1,
colour = shade,
size = size
)) +
geom_segment(show.legend = FALSE) +
coord_polar() +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(expand = c(0, 0)) +
scale_color_viridis_c() +
scale_size(range = c(0, 10)) +
theme_void()
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
polar_art <- function(seed, n, palette) {
# set the state of the random number generator
set.seed(seed)
# data frame containing random values for
# aesthetics we might want to use in the art
dat <- tibble(
x0 = runif(n),
y0 = runif(n),
x1 = x0 + runif(n, min = -.2, max = .2),
y1 = y0 + runif(n, min = -.2, max = .2),
shade = runif(n),
size = runif(n)
)
# plot segments in various colours, using
# polar coordinates and a gradient palette
dat |>
ggplot(aes(
x = x0,
y = y0,
xend = x1,
yend = y1,
colour = shade,
size = size
)) +
geom_segment(show.legend = FALSE) +
coord_polar() +
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(expand = c(0, 0)) +
scale_colour_gradientn(colours = palette) +
scale_size(range = c(0, 10)) +
theme_void()
}
polar_art(seed = 1, n = 500, palette = c("antiquewhite", "orange", "bisque"))
polar_art(seed = 1, n = 500, palette = c("red", "black", "white"))
polar_art(seed = 2, n = 50, palette = c("red", "black", "white"))
Exercise 2
1.
polar_art(4, 64, palette = c('red', 'white', 'blue'))
polar_art(4, 200, palette = c('red', 'white', 'blue'))
polar_art(110, 100, palette = c('salmon', 'lightyellow', 'lightblue'))
2.
polar_art2 <- function(seed, n, palette) {
# set the state of the random number generator
set.seed(seed)
# data frame containing random values for
# aesthetics we might want to use in the art
dat <- tibble(
x0 = runif(n),
y0 = runif(n),
x1 = x0 + runif(n, min = -.2, max = .2),
y1 = y0 + runif(n, min = -.2, max = .2),
shade = runif(n),
size = runif(n)
)
# plot segments in various colours, using
# polar coordinates and a gradient palette
dat |>
ggplot(aes(
x = x0,
y = y0,
xend = x1,
yend = y1,
colour = shade,
size = size
)) +
geom_segment(show.legend = FALSE) +
# removed the polar coordinate
scale_y_continuous(expand = c(0, 0)) +
scale_x_continuous(expand = c(0, 0)) +
scale_colour_gradientn(colours = palette) +
scale_size(range = c(0, 10)) +
theme_void()
}
polar_art2(1, 16, palette = c('red', 'white', 'blue'))
polar_art2(4, 200, palette = c('red', 'white', 'blue'))
polar_art2(110, 900, palette = c('salmon', 'lightyellow', 'lightblue'))
End of Exercise 2
library(scales)
library(ggthemes)
pal <- c("#cdb4db", "#ffc8dd", "#ffafcc", "#bde0fe", "#a2d2ff")
show_col(pal)
palette_fn <- colorRampPalette(pal)
palette_fn(100)
## [1] "#CDB4DB" "#CFB4DB" "#D1B5DB" "#D3B6DB" "#D5B7DB" "#D7B8DB" "#D9B8DB"
## [8] "#DBB9DB" "#DDBADB" "#DFBBDB" "#E1BCDB" "#E3BCDB" "#E5BDDB" "#E7BEDC"
## [15] "#E9BFDC" "#EBC0DC" "#EDC0DC" "#EFC1DC" "#F1C2DC" "#F3C3DC" "#F5C4DC"
## [22] "#F7C4DC" "#F9C5DC" "#FBC6DC" "#FDC7DC" "#FFC7DC" "#FFC6DC" "#FFC5DB"
## [29] "#FFC4DA" "#FFC3DA" "#FFC2D9" "#FFC1D8" "#FFC0D8" "#FFBFD7" "#FFBED6"
## [36] "#FFBDD5" "#FFBCD5" "#FFBBD4" "#FFBAD3" "#FFB9D3" "#FFB8D2" "#FFB7D1"
## [43] "#FFB6D1" "#FFB5D0" "#FFB4CF" "#FFB3CF" "#FFB2CE" "#FFB1CD" "#FFB0CD"
## [50] "#FFAFCC" "#FDAFCD" "#FBB1CF" "#F8B3D1" "#F5B5D3" "#F3B7D5" "#F0B9D7"
## [57] "#EDBBD9" "#EBBDDB" "#E8BFDD" "#E5C1DF" "#E3C3E1" "#E0C5E3" "#DDC7E5"
## [64] "#DBC9E7" "#D8CBE9" "#D5CDEB" "#D3CFED" "#D0D1EF" "#CDD3F1" "#CBD5F3"
## [71] "#C8D7F5" "#C5D9F7" "#C3DBF9" "#C0DDFB" "#BDDFFD" "#BCDFFE" "#BBDFFE"
## [78] "#BADEFE" "#B8DDFE" "#B7DDFE" "#B6DCFE" "#B5DCFE" "#B4DBFE" "#B3DBFE"
## [85] "#B2DAFE" "#B1D9FE" "#B0D9FE" "#AFD8FE" "#AED8FE" "#ACD7FE" "#ABD7FE"
## [92] "#AAD6FE" "#A9D5FE" "#A8D5FE" "#A7D4FE" "#A6D4FE" "#A5D3FE" "#A4D3FE"
## [99] "#A3D2FE" "#A2D2FF"
image(
x = matrix(1:100, ncol = 1),
col = palette_fn(100),
useRaster = TRUE,
axes = FALSE
)
canva_palettes[[101]]
## [1] "#4abdac" "#fc4a1a" "#f7b733" "#dfdce3"
show_col(canva_palettes[[101]])
sample_canva <- function(seed = NULL) {
if(!is.null(seed)) set.seed(seed)
sample(ggthemes::canva_palettes, 1)[[1]]
}
polar_art(seed = 2, n = 100, palette = sample_canva(seed = 2))
polar_art(seed = 2, n = 100, palette = sample_canva(seed = 3))
polar_art(seed = 2, n = 100, palette = sample_canva(seed = 4))
polar_art(seed = 5, n = 100, palette = sample_canva(seed = 1))
polar_art(seed = 6, n = 100, palette = sample_canva(seed = 1))
polar_art(seed = 7, n = 100, palette = sample_canva(seed = 1))
Exercise 3
1.
sample_canva(1)
## [1] "#fcc875" "#baa896" "#e6ccb5" "#e38b75"
sample_canva(2)
## [1] "#080706" "#efefef" "#d1b280" "#594d46"
sample_canva(3)
## [1] "#003b46" "#07575b" "#66a5ad" "#c4dfe6"
show_col(sample_canva(1))
show_col(sample_canva(2))
show_col(sample_canva(3))
2.
sample_named_colours <- function(n) {
colours <- colours(distinct = TRUE)
sampled_colours <- sample(colours, size = n)
return(sampled_colours)
}
# trying polar_art function with palette generator
polar_art(seed = 12, n = 1000, palette = sample_named_colours(8))
3.
random_palette <- function(n) {
colours <- unlist(ggthemes::canva_palettes)
sampled_colours <- sample(colours, size = n)
return(sampled_colours)
}
# trying polar_art function with palette generator
polar_art(seed = 12, n = 1000, palette = random_palette(8))
End of Exercise 3
sample_data <- function(seed = NULL, n = 100){
if(!is.null(seed)) set.seed(seed)
dat <- tibble(
x0 = runif(n),
y0 = runif(n),
x1 = x0 + runif(n, min = -.2, max = .2),
y1 = y0 + runif(n, min = -.2, max = .2),
shade = runif(n),
size = runif(n),
shape = factor(sample(0:22, size = n, replace = TRUE))
)
}
polar_styled_plot <- function(data = NULL, palette) {
ggplot(
data = data,
mapping = aes(
x = x0,
y = y0,
xend = x1,
yend = y1,
colour = shade,
size = size
)) +
coord_polar(clip = "off") +
scale_y_continuous(
expand = c(0, 0),
limits = c(0, 1),
oob = scales::oob_keep
) +
scale_x_continuous(
expand = c(0, 0),
limits = c(0, 1),
oob = scales::oob_keep
) +
scale_colour_gradientn(colours = palette) +
scale_size(range = c(0, 10)) +
theme_void() +
guides(
colour = guide_none(),
size = guide_none(),
fill = guide_none(),
shape = guide_none()
)
}
dat <- sample_data(n = 100, seed = 1)
pal <- sample_canva(seed = 1)
polar_styled_plot(data = dat, palette = pal) + geom_segment()
polar_styled_plot(data = dat, palette = pal) + geom_path()
polar_styled_plot(data = dat, palette = pal) + geom_point()
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
dat1 <- sample_data(n = 2000, seed = 123)
dat2 <- sample_data(n = 100, seed = 456) |>
mutate(y0 = .3 + y0 * .6, y1 = .3)
polar_styled_plot(palette = sample_canva(seed = 7)) +
geom_segment(
data = dat1 |> mutate(size = size * 3)
) +
geom_segment(
data = dat2 |> mutate(size = size / 5),
lineend = "round",
colour = "white"
) +
geom_segment(
data = dat2 |> mutate(size = size / 40),
lineend = "round",
colour = "#222222"
) +
geom_point(
data = dat2 |> mutate(size = size * 2),
colour = "#222222"
)
dat <- sample_data(n = 2000, seed = 123) |>
mutate(y1 = y0, size = size / 2)
polar_styled_plot(palette = sample_canva(seed = 456)) +
geom_segment(data = dat) +
geom_segment(data = dat |> mutate(y1 = y1 - .2, y0 = y0 - .2)) +
geom_segment(data = dat |> mutate(y1 = y1 - .4, y0 = y0 - .4))
dat <- sample_data(n = 1000, seed = 1) |>
mutate(y1 = y0, size = size / 4)
polar_styled_plot(palette = sample_canva(seed = 2)) +
geom_segment(data = dat, linetype = "331311")
Exercise 4
1.
dat3 <- sample_data(n = 1233, seed = 113) |>
mutate(y1 = y0, size = size * 14)
polar_styled_plot(palette = random_palette(3)) +
geom_segment(data = dat, linetype = "424242")
2.
normal_styled_plot <- function(data = NULL, palette) {
ggplot(
data = data,
mapping = aes(
x = x0,
y = y0,
xend = x1,
yend = y1,
colour = shade,
size = size *2
)) +
scale_y_continuous(
expand = c(0, 0),
limits = c(0, 1),
oob = scales::oob_keep
) +
scale_x_continuous(
expand = c(0, 0),
limits = c(0, 1),
oob = scales::oob_keep
) +
scale_colour_gradientn(colours = palette) +
scale_size(range = c(0, 10)) +
theme_void() +
guides(
colour = guide_none(),
size = guide_none(),
fill = guide_none(),
shape = guide_none()
)
}
# plots a similar thing, but NOT in polar coordinates
normal_styled_plot(palette = random_palette(4)) +
geom_segment(data = dat3, linetype = "424242")
End of Exercise 4
Task 2
library(dplyr)
library(purrr)
##
## Attaching package: 'purrr'
## The following object is masked from 'package:scales':
##
## discard
library(tibble)
library(ggplot2)
library(ggthemes)
library(ambient)
sample_canva <- function(seed = NULL) {
if(!is.null(seed)) set.seed(seed)
sample(ggthemes::canva_palettes, 1)[[1]]
}
sample_canva()
## [1] "#5f968e" "#bfdccf" "#e05858" "#d5c9b1"
sample_canva()
## [1] "#0f1626" "#ab987a" "#ff533d" "#f5f5f5"
sample_canva()
## [1] "#af4425" "#662e1c" "#ebdcb2" "#c9a66b"
sample_cross_matrix <- function(n = 10, seed = NULL) {
if(!is.null(seed)) set.seed(seed)
mat <- matrix(data = 0, nrow = n, ncol = n)
mat[sample(n, 1), ] <- 1
mat[, sample(n, 1)] <- 1
return(mat)
}
sample_cross_matrix()
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 0 0 0 0 0 0 0 1 0 0
## [2,] 0 0 0 0 0 0 0 1 0 0
## [3,] 0 0 0 0 0 0 0 1 0 0
## [4,] 0 0 0 0 0 0 0 1 0 0
## [5,] 0 0 0 0 0 0 0 1 0 0
## [6,] 0 0 0 0 0 0 0 1 0 0
## [7,] 0 0 0 0 0 0 0 1 0 0
## [8,] 1 1 1 1 1 1 1 1 1 1
## [9,] 0 0 0 0 0 0 0 1 0 0
## [10,] 0 0 0 0 0 0 0 1 0 0
image(sample_cross_matrix(n = 50), axes = FALSE, useRaster = TRUE)
image(sample_cross_matrix(n = 50), axes = FALSE, useRaster = TRUE)
image(sample_cross_matrix(n = 50), axes = FALSE, useRaster = TRUE)
x_coords <- seq(from = 0, to = 1, length.out = 800)
y_coords <- seq(from = 0, to = 1, length.out = 800)
canvas <- long_grid(x = x_coords, y = y_coords)
canvas
## # A tibble: 640,000 × 2
## x y
## <dbl> <dbl>
## 1 0 0
## 2 0 0.00125
## 3 0 0.00250
## 4 0 0.00375
## 5 0 0.00501
## 6 0 0.00626
## 7 0 0.00751
## 8 0 0.00876
## 9 0 0.0100
## 10 0 0.0113
## # ℹ 639,990 more rows
gen_perlin(x = 1:5, y = 1, frequency = .001, seed = 1)
## [1] -0.001000010 -0.001000010 -0.001000009 -0.001000009 -0.001000007
gen_perlin(x = 1:5, y = 1, frequency = .5, seed = 1)
## [1] -0.375 0.000 0.000 -0.250 0.000
canvas <- canvas |>
mutate(paint = gen_perlin(x, y, frequency = 10, seed = 1234))
canvas
## # A tibble: 640,000 × 3
## x y paint
## <dbl> <dbl> <dbl>
## 1 0 0 0
## 2 0 0.00125 0.0125
## 3 0 0.00250 0.0249
## 4 0 0.00375 0.0370
## 5 0 0.00501 0.0489
## 6 0 0.00626 0.0604
## 7 0 0.00751 0.0713
## 8 0 0.00876 0.0817
## 9 0 0.0100 0.0915
## 10 0 0.0113 0.101
## # ℹ 639,990 more rows
art <- ggplot(canvas, aes(x, y, fill = paint)) +
geom_raster(show.legend = FALSE)
art
art +
theme_void() +
coord_equal()
art +
theme_void() +
coord_equal() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradientn(colours = sample_canva())
Excerise 1
1.
# chanign gen_perlin() to get new results
canvas <- canvas |>
mutate(paint = gen_perlin(x, y, frequency = 1, seed = 3068))
canvas
## # A tibble: 640,000 × 3
## x y paint
## <dbl> <dbl> <dbl>
## 1 0 0 0
## 2 0 0.00125 -0.00125
## 3 0 0.00250 -0.00250
## 4 0 0.00375 -0.00375
## 5 0 0.00501 -0.00501
## 6 0 0.00626 -0.00626
## 7 0 0.00751 -0.00751
## 8 0 0.00876 -0.00876
## 9 0 0.0100 -0.0100
## 10 0 0.0113 -0.0113
## # ℹ 639,990 more rows
art <- ggplot(canvas, aes(x, y, fill = paint)) +
geom_raster(show.legend = FALSE)
art
art +
theme_void() +
coord_equal()
art +
theme_void() +
coord_equal() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0, 0)) +
scale_fill_gradientn(colours = sample_canva())
End of Exercise 1